Single-cell RNA sequencing analysis reveals that normal neural stem cells and glioblastoma cancer stem cells have fundamentally distinct transcriptome profiles that cluster into two separate populations. Both unsupervised hierarchical clustering and principal component analysis confirm clear transcriptional separation between these two cell types. [@zhao_single-cell_2019]

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Single-cell RNA sequencing has established that neural stem cells and glioblastoma cancer stem cells possess fundamentally distinct transcriptome profiles, revealing both the cellular heterogeneity within glioblastomas and the divergence between normal and malignant stem-like populations. The mechanistic basis for these transcriptional differences is primarily driven by copy number alterations, with gene expression scaling proportionally to whole chromosome copy number in chromosomally unstable cancer stem cells, such that most differential expression between the two cell types can be attributed to underlying genomic changes rather than alternative regulatory mechanisms. However, the interpretation of these transcriptomic signatures remains complicated by the confounding effects of proliferation, as proliferation-associated metagenes can obscure genuine biological differences and create spurious associations with clinical outcomes. While the relationship between genomic architecture and gene expression in glioblastoma stem cells is increasingly well-characterized, questions persist about how to disentangle true stem cell identity signatures from proliferation-driven expression patterns and how the continuum of stemness-related states observed in glioblastoma cells relates to discrete transcriptional programs.

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